Specimen integrity monitoring device for automated blood sample processing systems

ABSTRACT

Systems, methods, devices, and apparatus for detecting sample defects in blood samples processed in automated processing systems are described herein. One aspect describes an automated blood sample processing apparatus having a pre-analytic specimen integrity monitoring device. Another aspect describes devices, systems, and methods for identifying blood components and properties in blood samples. Further aspects relate to systems and methods for setting reference ranges for sample defects and interference in blood samples. Additionally, devices, systems, and methods for identifying defective samples are described.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No.14/986,321, entitled “SPECIMEN INTEGRITY MONITORING DEVICE FOR AUTOMATEDBLOOD SAMPLE PROCESSING SYSTEMS,” filed Dec. 31, 2015, which is relatedby subject matter to the following U.S. Patent Applications: U.S. patentapplication Ser. No. 14/986,392, entitled “IDENTIFYING LIQUID BLOODCOMPONENTS FROM SENSED DATA TO MONITOR SPECIMEN INTEGRITY,” filed Dec.31, 2015; U.S. patent application Ser. No. 14/986,432, entitled“MONITORING SPECIMEN INTEGRITY IN AUTOMATED BLOOD SAMPLE PROCESSINGSYSTEMS,” filed Dec. 31, 2015; U.S. patent application Ser. No.14/986,505, entitled “ESTABLISHING REFERENCE RANGES AND DETERMININGRESULTS FOR SAMPLES PROCESSED BY A SPECIMEN INTEGRITY MONITOR,” filedDec. 31, 2015; and U.S. patent application Ser. No. 14/986,511, entitled“Sample Gripping and Rotation Device for Automated Blood SampleProcessing Systems,” filed Dec. 31, 2015. Each of the aforementionedapplications is incorporated in this application by reference in itsentirety.

BACKGROUND

Traditionally, blood processing involved several individuals thathandled a given sample. Accordingly, prescreening of samples wastypically performed manually. However, as systems have moved towardautomation, fewer individuals handle the samples and, as a result,opportunities for manual visual inspection of samples have decreased.Automated systems for processing blood samples have failed to provide anadequate mechanism for prescreening defective samples. Consequently,improperly labeled specimens, improperly collected specimens andspecimens with various types of sample interference are commonlyprocessed in current automated systems. Accordingly, current systemoften process defective samples, resulting in sample errors and/orinaccurate results.

SUMMARY

Embodiments herein generally relate to devices, apparatus, systems, andmethods for monitoring for and detecting defective samples in automatedblood sample processing system. In one aspect, an automated blood sampleprocessing apparatus having a pre-analytic specimen integrity monitoringdevice is provided herein. The apparatus may include an automated systemcontrol for controlling routing parameters associated with a sample. Theautomated system control may route samples to the specimen integritymonitoring device for pre-analysis. The specimen integrity monitoringdevice may include and employ one or more sensors to capture sensorinformation, and a sample properties processor to determine one or moreproperties of the sample. For example, the specimen integrity monitoringdevice may detect one or more defects for the sample. In some aspects,the automated system control may route the sample to an error spur orerror lane for manual inspection when a defect has been detected in thesample.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments of the present invention are described indetail below with reference to the attached drawing figures, which areincorporated by reference herein and wherein:

FIG. 1 is a block diagram that depicts aspects of an operating systemenvironment suitable for practicing an embodiment of the invention;

FIG. 2 is a block diagram that depicts aspects of a computing systemsuitable for processing blood samples to determine sample properties anddefects, in accordance with some aspects herein;

FIG. 3 is a perspective view of an automated blood sample processingapparatus suitable to implement embodiments of the system illustrated inFIG. 2, and other aspects of the present invention;

FIG. 4 is a perspective view of a specimen integrity monitoring devicesuitable to implement embodiments of the system illustrated in FIG. 2,and other aspects of the present invention;

FIG. 5 is a flow diagram of a computer-implemented method foridentifying blood components and properties in blood samples andreference samples in an automated blood sample processing system, inaccordance with aspects herein;

FIG. 6 is a flow diagram illustrating a computer-implemented method forsetting one or more reference ranges for performing a pre-analysis ofblood samples to detect specimen defects in an automated blood sampleprocessing system, in accordance with aspects herein;

FIG. 7 is a flow diagram of a method for determining reference rangesand defects associated with blood samples in an automated blood sampleprocessing system, in accordance with aspects herein;

FIG. 8 is a flow diagram of a method for performing a pre-analysis ofblood samples to detect specimen defects and tube properties, inaccordance with aspects herein;

FIG. 9 is a flow diagram of a computer-implemented method fordetermining properties associated with a blood sample in an automatedblood sample processing system, in accordance with aspects herein;

FIG. 10 is a flow diagram of a method for configuring reference rangesettings for a specimen integrity monitor in an automated blood sampleprocessing system, in accordance with aspects herein;

FIG. 11 is a flow diagram of a method for modifying one or more existingreference ranges for a testable criteria in an automated blood testingsystem, in accordance with aspects herein; and

FIG. 12 is a flow diagram of a computer-implemented method for compilingresults from a specimen integrity monitor in an automated blood sampleprocessing system, in accordance with aspects herein.

DETAILED DESCRIPTION

The subject matter of the present invention is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different features orcombinations of features similar to the ones described in this document,in conjunction with other present or future technologies. Although theterms “step” and/or “block” might be used herein to connote differentelements of methods employed, the terms should not be interpreted asimplying any particular order among or between various steps hereindisclosed unless and except when the order of individual steps isexplicitly stated.

As briefly discussed hereinabove, aspects of this disclosure generallyrelate to detecting defects in blood samples processed in an automatedprocessing system(s). This disclosure provides devices and systems thatallow a defective sample to be automatically identified before it isprocessed.

At a high level, automated systems include sample routing tracks formoving samples throughout the system. The system may have an automatedsystem control (which will generally be designated hereinafter as an“ASC”) for controlling routing parameters associated with samples. Therouting parameters may be set based on sample type. For example, acoagulant sample may only be routed to a coagulometer, while a completeblood count sample, or serum for biochemical testing, may be routedthrough several analyzers. Accordingly, the ASC may operate, in part, tosend a sample to the appropriate analyzers. Accordingly, a sample may berouted to a specimen integrity monitor (which may be referred to as a“SIM”), based on routing parameters associated with the sample. The SIMgenerally operates to detect bad samples before they get to theanalyzers.

The SIM may identify defective samples using captured sensor informationto determine properties associated with the sample. A variety of sensorsmay be used to capture the information necessary to identify bloodproperties and defects. For example, the sensors may include highlycolor sensitive cameras. Accordingly, the sensors may include a redcolor detector, a blue color detector, and a green color detector, fordetecting color elements for the sample, among others. The sensedinformation from the sensors is then sent to one or more specializedprocessors to determine sample properties and defects.

Processing Sensed Information to Determine Sample Properties

Having provided the general context in which the embodiments disclosedherein employed, more particular aspects of the systems and methods fordetermining sample defects will be addressed. FIG. 2 shows an exemplarycomputing system 200 for processing samples to determine sampleproperties and defects.

Liquid components or portions of blood have a corresponding colorsand/or ranges of colors. Distances and linear space may be used todetermine how far apart color elements are, as a proxy for identifyingdifferences between colors in a blood sample. As a result, liquidcomponents of a sample may be identified by breaking the color elementsof the sample into color arrays. Values for the color arrays may then beused to determine a variety of types of interference or defects for thesample.

The system 200 may include a sample properties processor 230 that isconfigured to detect sample properties and identify liquid components ofblood samples. The sample properties processor 230 may receive, from theone or more sensors, a plurality of color elements for a sample. In someaspects, the plurality of color elements comprises one or more channelvalues. The channel values may include a red channel value; a bluechannel value; and a green channel value (“RBG” values). The channelvalues may be communicated by the sensors as a telegram, with delimitersfor separating the RBG values. In one aspect, the sensors send a stringof ASCII characters through Ethernet, as defined by telegram settings.The color elements may then be stored for further processing.

A linear array generator 232 may generally be configured for creating alinear array of the color elements. The linear array generator 232 maymap the color elements as a straight line, in order to determine adistance between the color elements. Euclidean distance is thestraight-line distance between two points in Euclidean space. Forexample, the Euclidean distance between points p and q is length of linesegment connecting them. Using the RBG channel values, the distancebetween colors may be determined. Further, the linear array generator232 may create a digital record of the plurality of color elements as alinear array.

As can be appreciated, although using Euclidean distance is one way ofdetermining differences or deviations between color elements (which mayalso be described as data elements having values corresponding to a hueor intensity of RBG values), any other suitable means for determiningcolor transitions is contemplated within the scope of the presentdisclosure. For example, a Chebyshev distance, a Mahalanobis distance,and variations thereof for determining distances or deviations betweenvectors or arrays, should be considered within the scope of theembodiments described herein.

The sample properties processor 230 may also include a backgroundidentifier 234 for determining one or more background color elements ofthe linear array. At a high level, the background identifier 234 maygenerally be responsible for determining and removing background colorelements from the linear array. In one aspect, the one or morebackground color elements are outside of a predetermined range of validcolor elements. The background elements may be color elements having abrightness outside of a predetermined valid brightness range.

Background color elements may be present in the sensed data due toshadows, sample barcodes, or ultraviolet light. Generally speaking,background color elements are color elements that do not occur within ablood sample and/or sample tube. Accordingly, in order to generateaccurate color arrays for a sample, the background color elements shouldbe removed. In one example, starting with the first color element of thelinear array, each color element may be analyzed to determine a firstcolor element within the valid brightness range. Continuing with thisexample, once the first valid color elements has been determined, thelinear array is analyzed from the opposite end to determine a firstcolor element within the valid brightness range. By way of illustration,the linear array may be analyzed from left to right for a first loop andfrom right to left for a second loop. Accordingly, any color elementsoutside of the valid brightness range may be trimmed or otherwiseremoved from the linear array, because they correspond to color elementsthat are not a part of the sample being analyzed. The remaining colorelements of the of the linear array may then be stored as a clean array,which no longer includes background colors or empty tube regions.

A distance array generator 236 may generally be responsible fordetermining a distance between each color element of the clean array.The distance between each color element may be determined using similarmeans to those used to determine the initial linear array. For example,a Euclidean distance between each color element may be determined.However, because noise/background color elements have been removed, thedistance between color elements now includes only color elements for thesample tube contents. The distances determined by the distance arraygenerator 236 may be stored as a distance array.

A transition indication array generator 238 may generally operate todetermine color transitions using the distances determined by thedistance array generator 236. In one example, the transition indicationarray generator 238 compares each distance to a transition indicationthreshold. When a distance is determined to be above the transitionindication threshold, the transition indication array generator 238indicates a color transition. In one aspect, a Boolean array with a sizeof n−1 may be generated. In this example, n is the size of the distancearray, or the linear array without outliers/background color elements.Accordingly, when a difference between one color element and asubsequent color element is above the threshold, the location may bemarked in an index of the Boolean array as true. The transitionindication array generator 238 may also operate to store the determinedtransitions as a transition indication array.

In some aspects, the transition indication array generator 238 may alsooperate to detect the presence of a barcode. Because a barcode hasnumerous dark and light regions in close proximity to one another, abarcode will result in a high number of transitions indications. In oneaspect, the transition indication array generator 238 may compare thetotal number of transitions in the array to the number of color elementsin the array. The resulting value may then be compared to a threshold todetermine a presence of a barcode. Accordingly, when the transitionsindicate the presence of a barcode, the values corresponding to thebarcode may be removed from the transition indication array in order toeliminate the barcode transitions from the transition indication array.Additionally, the values and color elements corresponding to the barcodemay be stored and used to identify the sample.

In additional aspects, a color boundary detector 240 may operate todetermine one or more boundary regions. In general, the transition fromone color to another in blood samples is gradual, because the liquidportions mix together and form an intermediate color. The transitionindication array may contain a number of transitions close to oneanother, that in actuality comprise a single color transition. Whentransition indications are above a predetermined frequency for a portionof the array, the portion of the array containing the high frequency oftransitions may be designated as a boundary region.

Boundary regions may be detected, for example, by determining if anothertransition indication is within the next four color elements of thearray. If another transition indication is found within the next fourelements, the transition indication is removed. The color boundarydetector 240 may determine a boundary region midpoint for each boundaryregion. The boundary regions may then be mapped as a single colortransition, at the boundary region midpoint, in the transitionindication array. This may be done iteratively. That is, a firsttransition indication may be determined, a second transition indicationmay be determined two color elements away, and a third transitionindication may be four color elements away from the second transitionindication. Continuing with the Boolean array example from above, eachof the first, second and third transition indications are set to false.However, the color element at the midpoint between the first transitionand second transition may be set as true. Accordingly, in this example,the boundary region, or region containing a mixture of two liquid bloodcomponents has been indicated as a single transition, rather than threeseparate transitions. As a result, the transition indication array maybe generated and indicated as an array that represents actualtransitions between the components/portions of the sample.

A liquid portions determiner 242 may generally operate to cut (orotherwise separate) the transition indication array at each colortransition to create a number of color arrays. Each color arraycorresponds to a liquid portion or component of the blood sample. Thisis so because each blood component, at a granular level, has a discreterange of color values. As described above, the transition indicationarray may now be an array without background color elements and anarray, which has each transition indication marked.

In one example, the transition indication array may be separated at eachtransition indication to form a number of individual color arrays. Eachcolor element of the color array may have an associated color value,which may be the RBG values of the color elements. Accordingly, eachcolor array has a range of color values which corresponds to a liquidportion of the blood sample.

Determining a color value for the color arrays may be accomplished inseveral ways. In one aspect, the color element in the middle of eachcolor array may be determined and used as a color value for the colorarray. Additionally, some color arrays may still contain mixture of twoliquid portions due to the boundary regions discussed above. In oneaspect, if the size of the array is above a threshold, color elementsmay be trimmed from either end of the array and discarded. The colorarray may be trimmed repeatedly until the array is a predetermined size.

Further, in some aspects, the liquid portions determiner 242 may beconfigured to determine an average or merged color for each color array.The majority of the true color of a given liquid blood component may belocated in the middle of a given color array. The merged color may alsobe determined by generating a normal distribution weighted average ofthe one or more color elements of the color array. The merged colorvalue may be used as the color value for the color array andcorresponding blood component.

Accordingly, using any of the above mechanisms, a number of color arraysfor each sample processed by the sample properties processor 230 may bedetermined. Further, each color array has a color value corresponding toa liquid blood property of the sample.

Generating Reference Ranges

The SIM may also be utilized to generate reference ranges for potentialsample defects. Reference ranges may be used by the SIM to define anacceptable range of color values for liquid portions of the sample. Thesample properties processor 230 may include a reference range determiner244, which generally operates to set reference range boundaries andgenerate the reference ranges. The values used for the reference rangeboundaries may be determined based on color arrays for referencesamples. The color arrays may be determined in the same manner asdetailed above.

Reference samples correspond to a testable criteria, or defect,associated with a liquid portion of the sample. For example, as will bediscussed in more detail below, the testable criteria may include one ormore of: a hematocrit criteria; a hemolysis criteria; a clottingcriteria; an icterus criteria; and a lipemia criteria. The referencesamples may have a predetermined minimum or maximum sample criteriavalue for samples analyzed in the automated blood sample processingsystem 210. For example, a reference sample may be fully analyzed todetermine a hematocrit value that represents a lower limit of hematocritvalues for acceptable samples. As will be appreciated, reference samplesmay also correspond to mean, median, or any other gradient indicator foracceptable sample. In other aspects, a visual inspection of thereference sample may indicate that the sample is an ideal sample for aminimum or maximum amount of red blood cells for acceptable samples.

Accordingly, a plurality of color elements for a first reference samplemay be sensed by the one or more sensors and communicated to the sampleproperties processor 230. Color arrays for the first reference samplemay then be determined, as detailed above. A color array thatcorresponds to the testable criteria may then be identified. Theidentified color array may be used by the reference range determiner 244to establish a first reference range boundary. As can be appreciated,the first reference range boundary has a corresponding color value,based on the color array. The first reference range boundary mayrepresent the minimum acceptable value. Accordingly, a second referencesample may be processed in the same way to determine a second referencerange boundary corresponding to the maximum acceptable value. Thereference range determiner 244 may then generate a reference range forthe testable criteria, based on the reference range boundaries.

Further, complete images of the reference samples may be stored andlater accessed, for example, for comparison to a sample. The referencerange determiner 244 may also generate a gradient or linear distributionof acceptable samples. Further, as can be appreciated, any number ofsamples may be run to establish reference range boundaries, medians, orany other reference range values. For example, 10 reference samples maybe analyzed to determine a minimum reference range boundary. Continuingwith this example, a mean color value, or a mean of the color arrayvalues for the 10 reference samples may be used as the color value forthe reference range boundary.

Sample Processing

Actual patient samples may be analyzed for defects, for example, by theblood sample processing system 210, as shown in FIG. 2. The blood sampleprocessing system 210 may include the ASC 212, which may route a givensample to the SIM 214, based on routing parameters associated with thesample in the ASC 212. Samples may be routed to the SIM 214 based on anynumber of criteria. For example, potassium assays are particularlysensitive to hemolysis. Accordingly, a patient sample that is beinganalyzed for potassium levels may automatically be routed to the SIM214. As can be appreciated, particularly in total lab automationsystems, it may be desirable to route each sample to the SIM 214 inorder to provide a mechanism for prescreening the samples for visibledefects.

Samples may be analyzed using the mechanisms detailed above, and mayemploy many of the same components as described above to do so. Forexample, the one or more sensors may capture a plurality of images ofthe sample, and associated data elements. A plurality of color elementsfor the sample may be determined from the images. Accordingly, the colorelements may be used to generate one or more color arrays for thesample, as described above. Further, the plurality of color elements maybe used to determine a tube cap color for the sample. The tube cap colormay be determined, for example, by a sample tube features determiner246, which may also be configured to determine a sample tube size.

Additionally, the sample properties processor 230 may determine a sampletype corresponding to the cap color. A given tube cap color may beassociated with a specific type of sample. For example, a light bluetube cap may be associated with a hematocrit sample type. Continuingwith this example, because the sample type is a hematocrit sample type,it may be determined that only defects that cause interference with ahematocrit sample need to be determined. Accordingly, the sampleproperties processor 230 may determine color arrays and use referenceranges that are associated with the specific defect. Associationsbetween cap colors and sample types may be stored in, for example, theASC 212. Additionally, the associations may be communicated to andstored by the sample properties processor 230.

The sample tube features determiner 246 may also be responsible fordetermining the barcode information from a barcode on the sample. Thesample properties processor 230 may also use the barcode information toretrieve a sample type for the sample. The sample properties processor230 may also compare the sample type and corresponding tests associatedwith a sample based on the barcode information, with the sample typeindicated by tube cap color and tube size determined by the sample tubefeatures determiner 246. Accordingly, a sample that has been placed inan incorrect sample tube may be identified by the SIM 214 and indicatedas defective.

Additionally, in aspects herein, a complete image of the sample and adigital record of all data elements associated with the sample may begenerated by the sample properties processor 230 and communicated to theASC 212. Although referred to as a processor, the sample propertiesprocessor 230 may have a dedicated local memory, or other suitable datastructure. Accordingly, sample data elements and result information mayalso be stored locally by the sample properties processor 230.

The sample properties processor 230, and its subcomponents, maydetermine color arrays and associated liquid portions of the sample. Thecolor arrays may be determined based on the plurality of color elements,as described hereinabove. Based on the sample type, the sampleproperties processor 230 may retrieve reference ranges for defects thatwill be analyzed. Accordingly, a defect identifier 248 may compare thedetermined color arrays for the sample, and associated values, to thereference ranges for a defect. As can be appreciated, when a color arrayis outside of the reference range, a defect may be indicated.

Just as sample types may have associated defects, each sample type mayalso require a minimum volume to perform a valid test. Accordingly, aminimum volume for each sample type may be predetermined and stored forcomparison to samples. The defect identifier 248 may also operate todetermine if the liquid volume of the sample is adequate. The volume ofthe sample may be determined, for example, by a sample tube featuresdeterminer 246. Determining the liquid volume of the sample may includeidentifying gel barriers; anticoagulants; serum; as well as any othersubstance in the sample tube other than the liquid blood. Accordingly,the liquid volume of the sample may include the tube volume less avolume of any substance that is not a liquid blood sample. The liquidvolume of the sample determined by the sample tube features determiner246 may be compared to the minimum by the defect identifier 248.Additionally, the sample tube features the 246 may be configured todetect a presence of air bubbles in the sample. Air bubbles may bepresent in improperly centrifuged samples and may cause interference ina variety of instruments.

Accordingly, when a defect is identified, the indication may becommunicated to the ASC 212, which may route the sample to an error ordefect holding lane. Further, all data elements associated with thesample and the identified defect may be communicated to the ASC 212 andany other aspects of the system via a network 106. The sample and defectinformation may be retained indefinitely or for a customizable of timefor additional comparison and analysis.

Types of Sample Defects

Defects and errors associated with blood samples may be caused byimproper collection, handling, and processing prior to analysis, amongmany other potential causes. Further, physiologic patient variables,such as increased or excessive amounts of a given blood component in apatient's blood, may also cause the sample defects. Processing sampleswith defects may lead to a sample error, which is only determined aftera full analysis of the sample has been completed. Further, processingdefective samples may also lead to inaccurate test results. Accordingly,processing defective blood samples may lead to unnecessary andpotentially inappropriate patient care, and increased costs due toanalyzing bad samples.

In one aspect, the sample properties processor 230 may analyze thesample to determine a level of hemolysis in the sample. When bloodsamples are improperly processed or handled, hemolysis may occur.Hemolysis causes red blood cells to rupture and release cytoplasm intosurrounding fluids. The presence of hemolysis may or may not compromisea sample depending on the type of test intended for the sample.Accordingly, a range of hemolysis that is acceptable may be set based onthe sample type and compared to the level of hemolysis in the sample todetermine if the sample is defective.

Additionally, the sample properties processor 230 may determine apercentage of red blood cells of the total sample volume, or ahematocrit value. The system may be configured to define an acceptablehematocrit value, so that the sample hematocrit value may be compared toa defined range. Based on the comparison, samples may be rejected orrouted for continued testing. Further, based on the color arraysgenerated for a sample, unacceptable levels of clotting may be detected.For example, the sample properties processor 230 may detect visibleclots, including red cell clots in whole blood, or fibrin clots inplasma.

Additionally, the sample properties processor 230 may analyze the samplefor an icterus defect. Icteric plasma contains high levels of bilirubin.Icteric plasma samples have a high prevalence in samples from intensivecare unit, gastroenterology, surgical, and pediatric patients. Highconcentrations of bilirubin can interfere with coagulation and othertypes of tests. Visible detection of icteric samples is sometimesdifficult. The sample properties processor 230 may detect ictericsamples prior to full testing in order to avoid testing a sample withinterference, which may result in an error. Further, the sampleproperties processor 230 may detect visible lipemia, or turbidity due toelevated concentrations of lipids. Lipemia usually translates to atriglyceride level >1000 mg/dL (whole blood) or >300 mg/dL (serum orplasma). Accordingly, reference ranges may be set at color values thatcorrespond to these to the concentrations above.

Further, the sample properties processor 230 may determine if a samplehas an inadequate volume. Using the sensed data, the sample propertiesprocessor 230 can determine a tube size and sample type. In one example,a cap color for the tube can be determined and used to detect a type ofsample being analyzed. The cap color may indicate the sample type, whichmay be stored in the ASC 212 and accessed by the sample propertiesprocessor 230. Gel barriers, serum and anticoagulant within the tube maybe detected. Additionally, the sample properties processor 230 maydetermine where the top of the blood sample is, and based on the gelbarriers, serum and anticoagulant, and sample type, determine whetherthe volume is adequate for the particular sample type. Additionally, insome aspects, the sample properties processor 230 may determinelocations of plasma and whole blood within a sample tube.

Post-Processing Analytics

The blood sample processing system 210 may also include a sample routingtrack 216 for controlling routing samples within the blood sampleprocessing system 210 and a user interface 218 to present informationrelating to sample defects and overrides of the defects by atechnologist. Additionally, the blood sample processing system 210 mayinclude one or more input devices 220 for receiving inputs from thetechnologist. Further, the blood sample processing system 210 mayinclude a system results compiler 222 that generally operates togenerate statistics and information relating to processed samples.

The system results compiler 220 may determine system results a number ofsamples processed by the sample properties processor 230. As can beappreciated, this may be accomplished in a number of ways. In oneaspect, the sample properties processor 230 may store and/or communicateindications of each sample processed. For example, a sample propertiesprocessor 230 may have access to a local storage device, and maycommunicate a number of samples processed to the ASC 212, or to alaboratory information system 112, or healthcare information system 110,via the network 106. Accordingly, the system results compiler 220 mayaccess the stored information for processed samples in order to compileresults.

Further, the system results compiler 220 may determine how many of eachtype of defect has been identified by the SIM 214. Using means similarto those described immediately above, the system results compiler 220may access data elements, stored indications, complete sample images,color values, and any number of other information associated withsamples identified as defective. For example, when a defect is detectedin a sample, the sample may be held for manual review by a technologist.In some aspects, the technologist must scan a badge or identificationcard using input device 220 or the user interface 218. Further, as atechnologist performs their review of samples, the identifications ofthe samples may automatically be associated with the technician.Accordingly, when a given technician overrides the sample propertiesprocessor's 230 determination that a sample is defective and routes thesample for continued processing, the override will automatically beassociated with the technician and the sample. Further, in some aspects,when an override command is received, the plurality of sample images,color values, color arrays, specimen variables, and any otherinformation associated with the sample, may be flagged and stored withan indication that the sample was associated with an override.

By compiling sample results, particularly for samples associated withoverrides, the overall system may be improved. This is so, in oneaspect, because tracking overrides essentially provides an ongoingmechanism for quality control regarding determinations made by the SIM214 and sample properties processor 230. Further, in some aspects, theuser interface 218 generates and displays an image comparison screen forcomparing the one or more images of a sample to a plurality of referenceimages. This can be an effective way of providing a real-time comparisonof a sample with references and/or standards. For example, an image of asample may be presented in a format that allows the technician to zoomin on the sample to obtain an optimal view of a sample color or colors.

Further, in some aspects, color values associated with the color arraysand corresponding blood properties or defects may be displayed with thesample images. This may also provide another quality controlopportunity. For example, if a color value associated with a sampledetermined to be defective is visibly incorrect from the perspective ofthe technologist, the technologist's override of the defect may indicatethat reference ranges for the defect should be adjusted. Additionally,compiling results for override may provide an indication that one of thesensors, or other physical components associated with the system needsto be inspected.

Further, in some aspects, the user interface 218 generates and displaysone or more modifiable reference ranges. In one aspect, the one or moreimages and/or color values for a sample may be added to a referencerange upon user selection. For example, a stored image for a processedsample may represent an ideal sample for setting a reference rangeboundary. The system provides a mechanism for adding the sample imagesand determined data elements to an existing reference range or may usethe images and/or data elements to establish a new reference range.

Exemplary Operating System Environment

Some aspects of the present invention may be described in the generalcontext of computer-executable instructions, such as program modules,being executed by a computer. Generally, program modules include, butare not limited to, routines, programs, objects, components, and datastructures that perform particular tasks or implement particularabstract data types. The present invention may also be practiced indistributed computing environments where tasks are performed by remoteprocessing devices that are linked through a communications network. Ina distributed computing environment, program modules may be located inlocal and/or remote computer storage media including, by way of exampleonly, memory storage devices.

As one skilled in the art will appreciate, embodiments of the presentinvention may be embodied as, among other things: a method, system, orset of instructions embodied on one or more computer-readable media.Accordingly, the embodiments may take the form of a hardware embodiment,a software embodiment, or an embodiment combining software and hardware.In one embodiment, the invention takes the form of a computer-programproduct that includes computer-usable instructions embodied on one ormore computer-readable media devices.

Computer-readable media include both volatile and nonvolatile media,removable and non-removable media, and contemplate media readable by adatabase, a switch, and various other network devices. By way ofexample, and not limitation, computer-readable media comprise mediaimplemented in any method or technology for storing information,including computer storage media and communication media. Examples ofstored information include computer-useable instructions, datastructures, program modules, and other data representations. Computerstorage media examples include, but are not limited to,information-delivery media, RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, digital versatile discs (DVDs), holographicmedia or other optical disc storage, magnetic cassettes, magnetic tape,magnetic disk storage, other magnetic storage devices, and othercomputer hardware or storage devices. These technologies can store datamomentarily, temporarily, or permanently.

While aspects of the present invention may be performed by specialpurpose computing devices (e.g., a sample properties processor 230 asdescribed in detail below), the special purpose devices may beoperational with general purpose devices and/or network configurations.Examples of well-known computing systems, environments, and/orconfigurations that may be suitable for use with the present inventioninclude, by way of example only, personal computers, server computers,handheld or laptop devices, multiprocessor systems, microprocessor-basedsystems, set top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, distributed computing environmentsthat include any of the above-mentioned systems or devices, and thelike.

Referring to the drawings in general, and initially to FIG. 1 inparticular, an exemplary computing system environment, on whichembodiments of the present invention may be implemented is illustratedand designated generally as reference numeral 100. It will be understoodand appreciated by those of ordinary skill in the art that theillustrated computing system environment 100 is merely an example of onesuitable computing environment and is not intended to suggest anylimitation as to the scope of use or functionality of the invention.Neither should the computing system environment 100 be interpreted ashaving any dependency or requirement relating to any single component orcombination of components illustrated therein.

With continued reference to FIG. 1, the exemplary computing systemenvironment 100 includes a general-purpose computing device in the formof a server 102. Components of the server 102 may include, withoutlimitation, a processing unit, internal system memory, and a suitablesystem bus for coupling various system components, including databasecluster 104, with the server 102. The system bus may be any of severaltypes of bus structures, including a memory bus or memory controller, aperipheral bus, and a local bus, using any of a variety of busarchitectures. By way of example, and not limitation, such architecturesinclude Industry Standard Architecture (ISA) bus, Micro ChannelArchitecture (MCA) bus, Enhanced ISA (EISA) bus, Video ElectronicStandards Association (VESA) local bus, and Peripheral ComponentInterconnect (PCI) bus, also known as Mezzanine bus.

The server 102 typically includes, or has access to, a variety ofdevices capable of storing computer-readable media, for instance,database cluster 104. Computer-readable media can be any available mediathat may be accessed by server 102, and includes volatile andnonvolatile media, as well as removable and non-removable media.Computer-readable media may be physically stored on any number ofdevices and/or data structures. By way of example, and not limitation,computer-readable media may include computer storage media andcommunication media. Computer storage media may include, withoutlimitation, volatile and nonvolatile media, as well as removable andnon-removable media implemented in any method or technology for storageof information, such as computer-readable instructions, data structures,program modules, or other data. In this regard, computer storage mediamay include, but is not limited to, RAM, ROM, EEPROM, flash memory orother memory technology, CD-ROM, digital versatile disks (DVDs) or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage, or other magnetic storage device, or any other medium which canbe used to store the desired information and which may be accessed bythe server 102. Computer storage media does not comprise signals per se.Communication media typically embodies computer-readable instructions,data structures, program modules, or other data in a modulated datasignal, such as a carrier wave or other transport mechanism, and mayinclude any information delivery media. As used herein, the term“modulated data signal” refers to a signal that has one or more of itsevaluation criteria set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared, and other wireless media. Combinations of any of the abovealso may be included within the scope of computer readable media.

The computer storage media discussed above and illustrated in FIG. 1,including database cluster 104, provide storage of computer-readableinstructions, data structures, program modules, and other data for theserver 102. The server 102 may operate in a computer network 106 usinglogical connections to one or more remote computers 108. Remotecomputers 108 may be located at a variety of locations so that anynumber of devices and device types may be capable of integration on thenetwork. The remote computers 108 may be personal computers, mobiledevices, servers, routers, network PCs, peer devices, other commonnetwork nodes, or the like, and may include some or all of thecomponents described above in relation to the server 102. The devicescan be personal digital assistants or other like devices.

Exemplary computer networks 106 may include, without limitation, localarea networks (LANs) and/or wide area networks (WANs). Such networkingenvironments are commonplace in offices, enterprise-wide computernetworks, intranets, and the Internet. When utilized in a WAN networkingenvironment, the server 102 may include a modem or other means forestablishing communications over the WAN, such as the Internet. In anetworked environment, program modules or portions thereof may be storedin the server 102, in the database cluster 104, or on any of the remotecomputers 108. For example, and not by way of limitation, variousapplication programs may reside on the memory associated with any one ormore of the remote computers 108. It will be appreciated by those ofordinary skill in the art that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers (e.g., server 102 and remote computers 108) may beutilized.

In operation, a user may enter commands and information into the server102 or convey the commands and information to the server 102 via one ormore of the remote computers 108 through input devices, such as akeyboard, a pointing device (commonly referred to as a mouse), atrackball, or a touch pad. Other input devices may include, withoutlimitation, microphones, satellite dishes, scanners, or the like.Commands and information may also be sent directly from a remotehealthcare device to the server 102. In addition to a monitor, theserver 102 and/or remote computers 108 may include other peripheraloutput devices, such as speakers and a printer.

Although many other internal components of the server 102 and the remotecomputers 108 are not shown, those of ordinary skill in the art willappreciate that such components and their interconnection are wellknown. Accordingly, additional details concerning the internalconstruction of the server 102 and the remote computers 108 are withinthe scope of this disclosure.

Exemplary computing system environment 100 may include a healthcareinformation system 110. The healthcare information system 110 mayoperate to store, receive, produce and communicate data elements relatedto the provision of healthcare. For example, the healthcare informationsystem 110 may receive orders, such as those for laboratory testing ofpatient blood samples. The orders may be received from clinicians.Clinicians may comprise a treating physician or physicians; specialistssuch as surgeons, radiologists, cardiologists, and oncologists;emergency medical technicians; physicians' assistants; nursepractitioners; nurses; nurses' aides; pharmacists; dieticians;microbiologists; laboratory experts; laboratory technologists; geneticcounselors; researchers; veterinarians; students; and the like. Theremote computers 108 may have access to the healthcare informationsystem 110. The remote computers 108 might be personal computers,servers, routers, network PCs, peer devices, other common network nodes,or the like and might comprise some or all of the elements describedabove in relation to the control server 102. The devices can be personaldigital assistants or other like devices.

Additionally, the exemplary computing system environment 100 may includea laboratory information system 112. The laboratory information system112 may operate to facilitate laboratory processing of the patient bloodsamples ordered in healthcare information system 110. For example, inone aspect, the laboratory information system 112 may receive anindication when a laboratory test is ordered for a patient. For example,when a complete blood panel is ordered for a patient, the laboratoryinformation system 112 may receive a notification that the completeblood panel has been ordered. Additionally, when a sample is collected,the healthcare information system 110 may communicate an indication of asample identification number or other indication of an identity of thesample and ordered tests associated with the sample. Accordingly, thelaboratory information system 112 may communicate the indication to theASC 212, which as discussed hereinabove, may route the sample forlaboratory analysis according to the ordered tests.

In some embodiments, laboratory information system 112 is a computingsystem made up of one or more computing devices. In an embodiment,laboratory information system 112 includes an adaptive multi-agentoperating system, but it will be appreciated that laboratory informationsystem 112 may also take the form of an adaptive single agent system ora non-agent system. Laboratory information system 112 may be adistributed computing system, a data processing system, a centralizedcomputing system, a single computer such as a desktop or laptop computeror a networked computing system.

In an embodiment, laboratory information system 112 is a multi-agentcomputer system with agents. A multi-agent system may be used to addressthe issues of distributed intelligence and interaction by providing thecapability to design and implement complex applications using formalmodeling to solve complex problems and divide and conquer these problemspaces. Whereas object-oriented systems comprise objects communicatingwith other objects using procedural messaging, agent-oriented systemsuse agents based on beliefs, capabilities and choices that communicatevia declarative messaging and use abstractions to allow for futureadaptations and flexibility. An agent has its own thread of control,which promotes the concept of autonomy.

Specimen Integrity Monitoring in an Automated Blood Sample ProcessingApparatus

As shown in FIG. 3, in one embodiment, an automated blood sampleprocessing apparatus 300 having a pre-analytic SIM is provided. Theapparatus 300 may comprise an automated system control (“ASC”) unit 310.The ASC unit may include the automated system control 212, as describedin detail with reference to FIG. 2, for controlling routing parametersassociated with a sample. For example, the ASC unit 310 may route thesample to a specimen integrity monitoring (“SIM”) device 340 forpre-analysis. In some aspects, the ASC unit 310 may route the sample toan error spur 324 or error lane when a defect has been detected in thesample. Further, in some aspects, the ASC unit 310 routes the sample forfull processing of ordered tests associated with the sample when adefect is not detected for the sample. Additionally, the ASC unit 310may be configured to track sample information, such as an identifier ofthe sample and defects/errors associated with the sample.

In some aspects, the automated blood sample processing apparatus 300 isa total lab automation system that may process a sample from beginningto end without user intervention, other than sample determined to bedefective. The apparatus 300 may include one or more sample routingtracks, e.g. 320 and 324, for moving samples throughout the apparatus300. In some aspects, the automated blood sample processing apparatus300 includes a main track 320 for routing the sample through the entireprocessing system. Additionally, in aspects herein, the automated bloodsample processing apparatus 300 has one or more spurs off the main track320. In one aspect, the apparatus 300 includes a SIM spur. The samplemay be routed to the SIM spur based on the routing parameters in ASCunit 310 associated with the sample. In another aspect, the SIM device340 may be mounted directly to the main track 320.

In one aspect, the automated blood sample processing apparatus 300 mayinclude the SIM device 340 for determining one or more properties of thesample. Accordingly, a sample may be routed to the SIM device 340, basedon routing parameters associated with the sample. SIM device 340 maycomprise a sample extraction device 342 for extracting the sample from asample holder for analysis. Sample extraction device 342 may also rotatethe sample for capturing sensor information for analyzing a sample. Insome aspects, the sample extraction device 342 may be configured torotate the sample 360° in order to capture a plurality of images of thesample. Additionally, the sample extraction device 342 may return thesample to the sample holder when the sensor information has beencaptured.

The SIM device 340 may include and employ one or more sensors 344 tocapture sensor information and a sample properties processor 230(described above with reference to FIG. 2) to determine one or moreproperties of the sample. In one aspect, the one or more sensors 344 areconfigured to capture a plurality of images of the sample. Additionally,the one or more sensors 344 may capture sensor information including aplurality of color elements for the sample. Further, the one or moresensors 344 may comprise a red color detector, a blue color detector,and a green color detector, for detecting the plurality of colorelements for the sample. In some aspects, the one or more sensors 344include a black and white camera for capturing barcode information onthe sample. In additional aspects, the one or more sensors 344 comprisea sample tube width detector; and a contour alignment detector formeasuring a tilt angle of the sample and aligning the one or moresensors 344. The sample tube width detector may determine a width of asample tube to determine a type of sample being processed. The contouralignment detector may operate to sense that a sample is off-center, ornot exactly vertically oriented, and communicate an orientation of thesample to the other sensors.

The SIM device 340 may also include a SIM control unit 346 that includesa variety of computing and communications devices. The SIM control unit346 may include a communications hub 347, which facilitates thiscommunication between the SIM device 340 and other components anddevices, such as ASC unit 310. The sample properties processor 230 maydetermine based on the plurality of images, a cap color and a liquidvolume of the sample. Further, in some aspects, the sample propertiesprocessor 230 receives the plurality of color elements and generates oneor more color arrays for the sample. The sample properties processor 230may also determine, based on the one or more color arrays and the liquidvolume of the sample, a presence of one or more defects in the sample.Additionally, upon determining a presence of the one or more defects,the sample properties processor 230 may communicate an indication of theone or more defects to the ASC unit 310. Further, in some aspects, thesample properties processor 230 creates a digital record of the barcodeinformation sensed by the one or more sensors 344 and communicates thedigital record of the barcode information to the ASC unit 310 fortracking information associated with the sample.

As mentioned above, the sample routing track may include an error spur324 for diverting the sample from the main track 320. The ASC unit 310may be configured to direct the sample to the error spur 324 based onthe indication of the one or more defects. The error spur 324 maygenerally operate to hold the sample for manual handling by atechnologist. The sample routing track may also include one or morerouting gates for transferring samples to and from the various tracksand spurs of the apparatus 300. For example, the sample routing trackmay include: a first electronically-actuated routing gate 322 forrouting the sample from the main track 320 to the SIM device 340; asecond electronically-actuated routing gate for routing the samplethrough the integrity monitor spur and returning the sample to the maintrack 320; and a third electronically-actuated routing gate 328 forrouting the sample from the main track 320 to the error spur 324. Theone or more routing gates may be electronically actuated, pneumaticallyactuated, or actuated using any other suitable means.

Turning now to FIG. 4, in another embodiment, a pre-analytic SIM device340 for detecting one or more defects associated with blood samples isprovided. The device may comprise one or more sensors 344, for capturinga plurality of images of a sample. The one or more sensors 344 mayinclude, for example, a red color detector, a blue color detector, and agreen color detector, for detecting a plurality of color elements forthe sample. In some aspects, the one or more sensors 344 include acontour alignment detector for measuring a tilt angle of the sample andaligning the one or more sensors 344. Further, in some aspects, the oneor more sensors 344 comprise a sample tube width detector. Additionally,as discussed above, the one or more sensors 344 may include a black andwhite camera for capturing barcode information on the sample.

In some aspects, the SIM device 340 includes a SIM control unit 346having a sample properties processor 230 that receives the plurality ofcolor elements and generates one or more color arrays. The sampleproperties processor 230 may determine based on the plurality of imagesof the sample, a cap color and a liquid volume of the sample. In someaspects, the sample properties processor 230 determines, based on theone or more color arrays and the liquid volume of the sample, if one ormore defects are present in the sample. Additionally, in aspects herein,the presence of the one or more defects is determined when at least onecolor array of the one or more color arrays or the liquid volume of thesample is outside of a predefined volume range for the sample. In oneaspect, the one or the defects determined by the a sample propertiesprocessor 230 comprise one or more of: a sample volume defect; ahematocrit defect; a hemolysis defect; a clotting defect; an icterusdefect; and a lipemia defect.

The SIM device 340 may also include a sample extraction device 342 forextracting the sample from a sample holder for analysis. Further, insome aspects, the sample extraction device 342 is configured to rotatethe sample for capturing sensor information and to return the sample tothe sample holder when the sensor information has been captured.Further, in some aspects, the SIM device 340 is configured to bemountable to a track of an existing automated blood sample processingapparatus. A sample lighting mechanism 349 for providing light while theplurality of images are captured may also be included in the SIM device340.

In another embodiment, a pre-analytic SIM device 340 configured forintegration with an existing automated blood sample processing apparatusis provided. In some aspects, the SIM device 340 for identifying apresence of one or more defects in one or more samples is provided. Inadditional aspects, upon identifying the presence of the one or moredefects, the SIM device 340 communicates an indication of the one ormore defects to the ASC unit 310.

In one aspect, the apparatus 300 includes an error spur 324 thatreceives one or more defective samples, the error spur 324 having aholding area for holding the one or more defective samples for manualinspection by a technologist. The apparatus may also include an ASC userinterface 360 that facilitates the manual inspection. The ASC userinterface 360 may be configured to present information relating tosample defects and overrides of the defects by the technologist.Additionally, the ASC user interface 360 may be configured to presentone or more selectable indications of reasons corresponding to anoverride command.

One or more processors of the ASC unit 310 may include one or morecomputer storage media storing computer-useable instructions that, whenused by the one or more processors, cause the one or more processors toperform operations. In one aspect, the operations comprise receiving anindication of an identity of the technologist via one or more ASC inputdevices 362. The indication of the identity of the technologist may bereceived via a scan of a badge associated with the technologist capturedusing the one or more ASC input devices 362. Additionally, theoperations may include presenting, on the user-interface, one or moreimages and an identification of the one or more defects associated withone or more defective samples. Further, the one or more processors mayreceive an override command corresponding to a sample of the one or moredefective samples. In some aspects, the operations comprise creating adigital record of the override command including the identity of thetechnologist, an identifier of the sample, and the indication of the oneor more defects. The ASC unit 310 may be configured to, for example,upon receiving the digital record of the override command, route thesample for continued processing.

Using Image Data Elements to Identify and Establish Reference Ranges forLiquid Blood Components

In one embodiment, as shown in FIGS. 2 and 5, a system for identifyingblood components and properties in blood samples and reference samplesin an automated blood sample processing system 210 is provided. In someaspects, the system may include one or more devices storingcomputer-useable instructions for performing operations in the automatedblood sample processing system 210. The system may comprise one or moresensors for capturing sensor data elements for one or more samples. Thesensors may include, for example as discussed in more detailhereinabove, a red color detector, a blue color detector, and a greencolor detector, for detecting the plurality of color elements for thesample. In some aspects, the one or more sensors include a black andwhite camera for capturing barcode information on the sample. Inadditional aspects, the one or more sensors comprise a sample tube widthdetector; and a contour alignment detector for measuring a tilt angle ofthe sample and aligning the one or more sensors. The sample tube widthdetector may, for example, determine a width of a sample tube todetermine a type of sample being processed. The contour alignmentdetector may operate to sense that a sample is off-center, or notexactly vertically oriented, and communicate an orientation of thesample to the other sensors.

The system may comprise a sample properties processor 230 having one ormore components for identifying sample properties. In some aspects, thesystem includes one or more computer storage media storingcomputer-useable instructions that, when used by the sample propertiesprocessor 230, cause the sample properties processor 230 to performoperations. The operations may a method 500 for identifying sampleproperties, as shown in FIG. 5.

At block 502, in one aspect, the operations may comprise receiving, fromthe one or more sensors, a plurality of color elements for a sample. Insome aspects, the plurality of color elements comprises one or morechannel values. The channel values may include: a red channel value; ablue channel value; and a green channel value. The channel values may becommunicated by the sensors as a telegram, with delimiters forseparating the RB G values.

As shown at block 504, in some aspects, a linear array generator 232 maygenerally be configured for creating a digital record of the pluralityof color elements as a linear array. The linear array generator 232 maymap the color elements as a straight line, in order to determine adistance between the color elements. Accordingly, distances and linearspace may be used to determine how far apart color elements are, as aproxy for identifying differences between colors.

The operations may also include creating a clean array, as illustratedat block 508. Creating the clean array may comprise determining, by abackground identifier 234, one or more background color elements of thelinear array. In one aspect, the one or more background color elementsare outside of a predetermined range of valid color elements. In someaspects, the background elements are color elements having a brightnessoutside of a predetermined valid brightness range. In some aspects, theoperations may include removing, by the background identifier 234, theone or more background color elements from the linear array.Additionally, in aspects herein, the operations may include creating adigital record of the remaining color elements of the plurality of colorelements as a clean array.

Further, as illustrated at block 510, the operations may includecreating a distance array. Creating a distance array may comprisedetermining, by a distance array generator 236, a distance between eachcolor element of the clean array. Additionally, in some aspects, theoperations include creating, by the distance array generator 236, adigital record of the distance between each color element as a distancearray. In one aspect, as described in detail with reference to FIG. 2above, the distance between the plurality of color elements may bedetermined using a Euclidean distance formula.

As shown at block 512 additionally, the operations may include creatinga transition indication array. Creating the transition indication arraymay include, determining, by a transition indication array generator238, if each distance of the distance array is above a transitionindication threshold. In aspects herein, a distance above the transitionindication threshold indicates a color transition. Further, thetransition indication array generator 238 may create a digital record ofthe transition indication array. In additional aspects, the distancebetween each transition indication is compared to a transition frequencythreshold to determine one or more boundary regions. Further, in someaspects, a boundary region midpoint is determined for each boundaryregion of the one or more boundary regions and mapped as a single colortransition in the transition indication array.

In one aspect, as illustrated at block 514, a liquid portions determiner242 may generally operate to cut (or otherwise separate) the transitionindication array at each color transition to create one or more colorarrays. Each color array may have a color value corresponding to aliquid blood property of the sample.

Further, in some aspects, the liquid portions determiner 242 may beconfigured to determine a merged color for each color array. The mergedcolor generally represents a testable value for the blood component. Themerged color may also be determined by generating a normal distributionweighted average of the one or more color elements of the color array.Additionally, a liquid portions determiner 242 may be configured fordetermining a number of color elements in each color array, and removingcolor elements beyond a color array size threshold.

In another embodiment, as shown in FIGS. 2 and 6, a system for settingone or more reference ranges for performing a pre-analysis of bloodsamples to detect specimen defects in an automated blood sampleprocessing system 210 is provided. In some aspects, the system mayinclude one or more devices storing computer-useable instructions forperforming operations in the automated blood sample processing system210. The system may comprise one or more sensors for capturing sensordata elements for one or more samples. As described in more detailhereinabove, the system may comprise a variety of sensors. In anonlimiting example, the sensors may include RGB detectors, a sampletube width detector, and/or a contour alignment detector.

The system may comprise a sample properties processor 230 having one ormore components for identifying sample properties. In some aspects, thesystem includes one or more computer storage media storingcomputer-useable instructions that, when used by the sample propertiesprocessor 230, cause the sample properties processor 230 to performoperations. The operations may a method 600 for setting one or morereference ranges for performing a pre-analysis of blood samples todetect specimen defects, as shown in FIG. 6.

As shown in FIG. 6, at block 602, the operations may comprise capturing,by one or more sensors, a plurality of color elements for a firstreference sample. In aspects herein, the first reference samplecorresponds to a testable criteria associated with a liquid bloodcomponent. As discussed in more detail above, the testable criteria mayinclude one or more of: a hematocrit criteria; a hemolysis criteria; aclotting criteria; an icterus criteria; and a lipemia criteria.

As shown at block 604, in one aspect, the operations may comprisedetermining, by a liquid portions determiner 242, a color array for thefirst reference sample, the color array for the first reference samplecomprising one or more color elements of the plurality of color elementsfor the first reference sample.

In some aspects, as shown at block 606, the operations includedetermining a first reference range boundary. The first reference rangeboundary may be determined by a reference range determiner 244, based onthe color array for the first reference sample. Further, in someaspects, the operations include creating a digital record of the firstreference range boundary. In some aspects, the first reference rangeboundary has a corresponding color value, based on the color array.Additionally, in aspects herein, the first reference sample is a samplehaving a predetermined minimum sample criteria value for samplesanalyzed in the automated blood sample processing system 210. As can beappreciated, any number of samples may be run to establish referencerange boundaries. In this scenario, a mean color value, or a mean of thecolor arrays for a plurality of reference samples may be used as thecolor value for the range boundary.

As shown at block 608, in one aspect, the operations may also includecapturing, by the one or more sensors, a plurality of color elements fora second reference sample. In aspects herein, the second referencesample corresponds to the testable criteria, which is the same testablecriteria that is associated with the first reference sample.

Further, as shown at block 610, the operations may include determining,by the liquid portions determiner 242, a color array for the secondreference sample, the color array for the second reference samplecomprising one or more color elements of the plurality of color elementsfor the second reference sample. In one aspect, the second referencesample is a sample having a predetermined maximum sample criteria valuefor samples analyzed in the automated blood sample processing system210. The operations may also include, as shown at block 612,determining, by the reference range determiner 244, based on the colorarray for the second reference sample, a second reference range boundaryand creating a digital record of the second reference range boundary.The second reference range boundary may also have a corresponding colorvalue.

As shown at block 614, further, in some aspects, the operations maycomprise generating a reference range for the testable criteria. Thereference range may be generated by the reference range determiner 244,and may comprise the first reference range boundary and the secondreference range boundary. As mentioned above, the operations may alsoinclude processing a plurality of reference samples within the referencerange to determine a plurality of color array value for the referencerange. Further, in some aspects, the reference range determiner 244 maygenerate a gradient of color array values between the first referencerange boundary and the second reference range boundary for assigning acolor array value to the sample.

Turning now to FIG. 7, an additional embodiment provides a method 700for determining reference ranges and defects associated with bloodsamples in an automated blood sample processing system 210. As shown atblock 702, the method may comprise determining a plurality of colorelements for a first reference sample and a second reference sample. Thecolor elements may be determined based on sensor data elements capturedby one or more sensors. Additionally, the first reference sample and thesecond reference sample may correspond to one or more testable criteria.Additionally, the one or more testable criteria may be associated withone or more liquid blood properties. For example, the criteria maycorrespond to a hematocrit criteria; a hemolysis criteria; a clottingcriteria; an icterus criteria; and a lipemia criteria.

As shown at block 704, in some aspects, the method comprises creating afirst reference sample array and a second reference sample array, basedon the plurality of color elements. In one aspect, as shown at block706, the method comprises determining one or more color transitions forthe first reference sample array and for the second reference samplearray. As shown at block 708, the method may also include creating oneor more color arrays for the first reference sample and one or morecolor arrays for the second reference sample, the one or more colorarrays having color values corresponding to one or more liquid bloodproperties. In some aspects, the one or more color arrays are created bycutting the first reference sample array and the second reference samplearray at each color transition of the one or more color transitions. Asshown at block 710, the method may include creating a reference rangefor a testable criteria, based on the color values associated with thereference range boundaries. Further, a digital record of the referencerange may be created. In one aspect, the first reference sample has apredetermined minimum testable criteria value for the reference rangeand the second reference sample has a predetermined maximum testablecriteria value for the reference range.

In some aspects, as shown at block 712, the method comprisesdetermining, for a sample, one or more sample color arrays. In someaspects, the one or more sample color arrays have color valuescorresponding to the one or more liquid blood properties. As shown atblock 714, the method may comprise comparing a sample color array of theone or more sample color arrays with the reference ranges for the one ormore color arrays. Additionally, as shown at block 716, the method mayinclude determining a presence of one or more defects in the sample. Thepresence of the one or more defects may be determined when the samplecolor array is outside of the reference range for the testable criteria.As shown at block 718, in some aspects, the method includes,communicating a readable error code including an indication of the oneor more defects, to an automated method control. The readable error codeincluding the indication may be communicated, upon determining apresence of the one or more defects.

As can be appreciated, the steps of the method 700 may be accomplishedor carried out in the one or more components of the system 200 describedhereinabove with reference to FIG. 2. Accordingly, a system thatimplements the steps described in method 700 via the processors andcomponents described hereinabove should be considered within the scopeof the present disclosure.

Identifying Sample Defects

In one embodiment, as shown in FIG. 8, a method 800 for performingpre-analysis of blood samples to detect specimen defects and tubeproperties is provided. As shown at block 802, the method may comprisecapturing, by one or more sensors, a plurality of images of a sample. Insome aspects, as shown at block 804, the method includes determining,various properties of the sample based on the plurality of images. Inone aspect, a plurality of color elements for the sample are determined,based on the images. In some aspects, as shown at block 806, a tube sizeand a liquid volume of the sample are also determined. Further, in someaspects, the method may include capturing barcode information from abarcode on the sample. Additionally, in some aspects, the sampleproperties processor 230 creates a digital record of the barcodeinformation and communicates the digital record of the barcodeinformation to an ASC 212 for tracking sample information.

As shown at block 808, in some aspects, the method includes determining,based on the plurality of color elements, a cap color of the sample.Additionally, in some aspects, the method includes determining a sampletype corresponding to the cap color. A given color may be associatedwith a specific type of sample. Associations between cap colors andsample types may be stored in, for example, the ASC 212. Additionally,the associations may be communicated to and stored by the sampleproperties processor 230. Additionally, in aspects herein, a completeimage of the sample and a digital record of the complete image may becreated, based on the plurality of images. Further, in some aspects, themethod comprises communicating the digital record to the ASC 212.

In one aspect, as shown at block 810, the method includes determining,based on the sample type, one or more defects to be analyzed for thesample type. For example, a light blue tube cap may be associated with ahematocrit sample type. Continuing with this example, because the sampletype is a hematocrit sample type, it may be determined that only defectsthat cause interference with a hematocrit sample need to be determined.In some aspects, the one or more defects may include one or more of: avolume defect; a hematocrit defect; a hemolysis defect; a clottingdefect; an icterus defect; and a lipemia defect.

As shown at block 812, the method may include determining if the liquidvolume of the sample outside of a predefined volume range associatedwith the sample type. Just as sample types may have associated defects,each sample type may also require a minimum volume to perform a validtest. Accordingly, a minimum volume for each sample type may bepredetermined and stored for comparison to samples. As a result, methodmay further include rejecting the sample when the liquid volume is belowa minimum volume associated with the predefined volume range.

The method may also include, as shown at block 814, determining, one ormore color arrays for the sample. The color arrays may be determinedbased on the plurality of color elements, as described hereinabove.Further, each color array may correspond to a defect of the one or moredefects. Lastly, as shown at block 816, the method may includedetermining if the each color array is outside of a reference range fora corresponding defect. As can be appreciated, when a color array isoutside of the reference range, a defect may be indicated. Accordingly,the method may also comprise rejecting the sample when one or more colorarrays is outside a reference range for a corresponding defect.

In another embodiment, as shown in FIGS. 2 and 9, a system fordetermining properties associated with a blood sample in an automatedblood sample processing system 210 is provided. In some aspects, thesystem may include one or more devices storing computer-useableinstructions for performing operations in the automated blood sampleprocessing system 210. The system may comprise a sample propertiesprocessor 230. In some aspects, one or more computer storage mediastoring computer-useable instructions that, when used by the sampleproperties processor 230, cause the sample properties processor 230 toperform operations, as illustrated in FIG. 9. As can be appreciated, theoperations may comprise a method 900 for determining sample properties.

In some aspects, as shown at block 902, the operations includedetermining, by a sample tube features determiner 246, a tube cap color,and a liquid volume of the sample. In some aspects, the tube cap colorand the liquid volume may be determined based on the one or more images.Further, in one aspect, a plurality of color elements for the sample maybe determined from the one or more images to determine the tube capcolor and the liquid volume of the sample, as described hereinabove withreference to FIG. 2. In one aspect, determining the liquid volume of thesample may include identifying, based on the one or more images, one ormore of: a gel barrier; an anticoagulant; and a serum. In some aspects,the liquid volume of the sample comprises a volume of liquid blood abovethe gel barrier, the anticoagulant; and/or the serum.

As illustrated at block 904, in one aspect, the operations includegenerating, by a liquid portions determiner 242, one or more colorarrays. As described hereinabove for FIG. 2, the one or more colorarrays may correspond to liquid portions of the blood sample associatedwith sample defects and/or interference. Additionally, as shown at block906, the operations may include identifying, by a defect identifier 248,based on the one or more color arrays and the liquid volume of thesample, a presence of one or more defects in the sample.

As illustrated at block 908, in some aspects, the defect identifier 248may communicate a readable error code including an indication of the oneor more defects, to an ASC 212. As can be appreciated, the readableerror code including the indication may be communicated upon identifyingthe presence of the one or more defect. In additional aspects, a digitalrecord of the readable error code and an identification of the samplemay be created and communicated to facilitate tracking and routing ofthe sample.

Further, as shown at block 910, the automated system control maygenerate a user interface for a manual inspection of the sample. Asillustrated at block 912, the automated system control may also create acomplete image of the sample, based on the one or more images.Additionally, as illustrated at block 914, the automated system controlmay retrieve one or more reference sample images. In some aspects, theone or more reference sample images comprise one or more images of oneor more reference samples corresponding to the one or more defects. Saidanother way, the reference sample images may be images of referencesamples for the defect identified in the sample.

As shown at block 916, generating and presenting an image comparisonscreen for comparing the complete image of the sample to the one or morereference sample images. In some aspects, the method may includeretrieving, by the one or more processors, one or more additionalcomplete images associated with one or more additional samples.Additionally, in one aspect, the user interface includes a sample searchfeature for retrieving sample data elements based on one or more of anaccession number associated with a processed sample or a defectassociated with the processed sample. Further, the user interface may bepresented on a touchscreen interface that facilitates navigation of theuser interface using a plurality of touch gestures. For example, a usermay zoom-in on a portion of a sample for a reference sample image.Further, the user interface may display one or more color valuesassociated with the sample, the one or more additional samples, or theone or more reference sample images on the image comparison screen.

Configuring SIM Reference Ranges and Compiling SIM Results

One embodiment herein, as shown in FIG. 10, comprises a method 1000 forconfiguring reference range settings for a specimen integrity monitor inan automated blood sample processing system. As illustrated at block1002, the method may comprise generating and presenting a user interfacefor creating one or more reference ranges for one or more testablecriteria. Further, as shown at block 1004, the method may includereceiving, via the user interface, a selection of a reference range ofthe one or more reference ranges.

As illustrated at block 1006, the method may comprise determining, for aplurality of reference samples corresponding to the selected referencerange, one or more color arrays. As illustrated at block 1008, themethod may include determining one or more color values corresponding tothe one or more color arrays. In some aspects, the one or more colorarrays are determined based on a plurality of color elements for eachreference sample of the plurality of reference samples captured by oneor more sensors.

As illustrated at block 1010, the method may also comprise determining afirst reference range boundary, the first reference range boundarycorresponding to a first color value of the one or more color values.Accordingly, as shown at block 1012, the method may include determininga second reference range boundary, the second reference range boundarycorresponding to a second color value of the one or more color values.In some aspects, the first color value corresponds to a minimum colorvalue for the testable criteria. In one aspect, the second color valuecorresponds to a maximum color value for the testable criteria.

As illustrated at block 1014, the method may include generating adistribution of the one or more color values between the first referencerange boundary and the second reference range boundary. In some aspects,the distribution comprises one or more color values between the minimumcolor value and the maximum color value. Additionally, as shown at block1016, the method may also include generating a reference range for atestable criteria, the reference range comprising the first referencerange boundary, the second reference range boundary, and thedistribution.

Another embodiment herein provides a method 1100 for modifying one ormore existing reference ranges for a testable criteria in an automatedblood testing system, as illustrated in FIG. 11. As illustrated at block1102, the method may comprise presenting one or more modifiablereference ranges, the one or more modifiable reference ranges having aplurality of existing color values. Further, as shown at block 1104, themethod may include receiving a user selection of a modifiable referencerange of the one or more modifiable reference ranges. As illustrated atblock 1106, the method may comprise receiving one or more additionalcolor values for one or more additional reference samples.

Additionally, as shown at block 1108, the method may also include addingthe one or more additional color values to the selected modifiablereference range. Adding the one or more additional color values to theselected reference range may include determining a first reference rangeboundary, the first reference range boundary corresponding to a firstcolor value of the one or more additional color values and the pluralityof existing color values. Accordingly, adding the additional colorvalues may also comprise determining a second reference range boundary,the second reference range boundary corresponding to a second colorvalue of the one or more additional color values and the plurality ofexisting color values. As can be appreciated, a distribution of the oneor more additional color values and the plurality of existing colorvalues between the first reference range boundaries may also begenerated. Further, adding the one or more additional color values mayinclude generating an updated reference range for a testable criteria,the reference range comprising the first reference range boundary, thesecond reference range boundary, and creating a digital record of theupdated reference range.

Turning now to FIG. 12, an additional embodiment provides a system forcompiling results from a specimen integrity monitor in an automatedblood sample processing system. In some aspects, the system may includeone or more devices storing computer-useable instructions for performingoperations in the automated blood sample processing system 210. As canbe appreciated, the operations carried out by the system may comprise amethod 1200. The system may comprise one or more sensors configured toprovide sensor data for a plurality of samples. In some aspects, thesystem includes a sample properties processor 230 for performing, basedon the sensor data for each sample of the plurality of samples,operations for determining one or more data elements associated witheach sample. The operations described with respect to block 1202-1212will be addressed briefly, as each operation has been described indetail hereinabove.

The operations may include, as shown at block 1202, generating one ormore images of the sample. Further, as shown at block 1204, theoperations may comprise determining an identification of the sample. Asillustrated at block 1206, the operations include determining a sampletype for the sample. As illustrated at block 1208, a presence of one ormore defects in the sample may be identified. In some aspects, as shownat block 1210, the operations include generating a readable error codeincluding an indication of the one or more defects. As illustrated atblock 1212, the operations may also include creating a digital record ofthe one or more data elements.

Additionally, the system may include an ASC 212 having one or moreprocessors for generating SIM results. Further, the system may compriseone or more computer storage media storing computer-useable instructionsthat, when used by the one or more processors, cause the one or moreprocessors to perform operations.

In some aspects, the operations comprise determining, by a systemresults compiler 220, a number of samples processed by the sampleproperties processor 230, as shown at block 1214. As can be appreciated,this may be accomplished in a number of ways. In one aspect, the sampleproperties processor 230 may store and/or communicate indications ofeach sample processed. For example, a sample properties processor 230may have access to a local storage device, and may communicate a numberof samples processed to the ASC 212, or to a laboratory informationsystem 112 or healthcare information system 110, via network 106.Accordingly, the system results compiler 220 may access the storedinformation for processed samples in order to compile results.

Further, as shown at block 1216, the operations may include determining,by the system results compiler 220, a number of defects identified foreach defect of the one or more defects. In some aspects, the defectscomprise one or more of: a volume defect; a hematocrit defect; ahemolysis defect; a clotting defect; an icterus defect; and a lipemiadefect. Using means similar to those described immediately above, thesystem results compiler 220 may access data elements, storedindications, complete sample images, color values, and any number ofother information associated with samples identified as defective.

As illustrated at block 1218, the operations may include determining, bythe system results compiler 220, one or more specimen variables for eachsample of the plurality of samples. The one or more specimen variablesmay be determined, in one aspect, based on the identification of thesample. In some aspects, the one or more specimen variables comprise oneor more of: a collection location; an identification a phlebotomistassociated with the sample; sample handling information; and sampleprocessing information.

In one aspect, as shown at block 1220, the operations includedetermining, by the system results compiler 220, a plurality of dataelements associated with one or more override commands. In some aspects,the plurality of data elements associated with one or more overridecommands comprise one or more of: a number of override commands; one ormore defects associated with the one or more override commands; atechnologist associated with the one or more override commands; and oneor more reasons corresponding to the override command. In some aspects,the system results compiler 220 may access override command data storedin the ASC 212, or other suitable locations within the operatingenvironment and computing systems described hereinabove with referenceto FIGS. 1 and 2.

For example, when a defect is detected in a sample, the sample may beheld for manual review by a technologist. In some aspects, thetechnologist must scan a badge or identification card using input deviceor the user interface 216. Further, as a technologist performs theirreview of samples, the identifications of the samples may automaticallybe associated with the technician. Accordingly, when a given technicianoverrides the sample properties processor's 230 determination that asample is defective and routes the sample for continued processing, theoverride automatically be associated with the technician and the sample.Further, in some aspects, when an override command is received, theplurality of sample images, color values, color arrays, specimenvariables, and any other information associated with the sample, may beflagged and stored with an indication that the sample was associatedwith an override. By compiling sample results, particularly for samplesassociated with overrides, the overall system may be improved. This isso, in one aspect, because tracking overrides essentially provides anongoing mechanism for quality control regarding determinations made bythe SIM 214 and the sample properties processor 230.

Additionally, as shown at block 1222, the operations may includegenerating, by the ASC 212, a user interface 216 for interacting withthe SIM results. In some aspects, the plurality of data elementsassociated with one or more override commands comprise one or more of: anumber of override commands; one or more defects associated with the oneor more override commands; a technologist associated with the one ormore override commands; and one or more reasons corresponding to theoverride command. In some aspects, the SIM results comprise one or morestatistics, including one or more of: a number of samples processedduring a customizable time period; a number defects for each type ofdefect identified during the customizable time period; a number of eachtype of defect that originated from one or more collection locations;and a number of each type of defect associated with a particularphlebotomist.

For example, when a defect is detected in a sample, the sample may beheld for manual review by a technologist. In some aspects, thetechnologist must scan a badge or identification card using input deviceor the user interface 218. Further, as a technologist performs theirreview of samples, the identifications of the samples may automaticallybe associated with the technician. Accordingly, when a given technicianoverrides the sample properties processor's 230 determination that asample is defective and routes the sample for continued processing, theoverride automatically be associated with the technician and the sample.Further, in some aspects, when an override command is received, theplurality of sample images, color values, color arrays, specimenvariables, and any other information associated with the sample, may beflagged and stored with an indication that the sample was associatedwith an override. By compiling sample results, particularly for samplesassociated with overrides, the overall system may be improved. This isso, in one aspect, because tracking overrides essentially provides anongoing mechanism for quality control regarding determinations made bythe SIM 214 and the sample properties processor 230.

Additionally, as shown at block 1222, the operations may includegenerating, by the ASC 212, a user interface 218 for interacting withthe SIM results. In some aspects, the plurality of data elementsassociated with one or more override commands comprise one or more of: anumber of override commands; one or more defects associated with the oneor more override commands; a technologist associated with the one ormore override commands; and one or more reasons corresponding to theoverride command. In some aspects, the SIM results comprise one or morestatistics, including one or more of: a number of samples processedduring a customizable time period; a number defects for each type ofdefect identified during the customizable time period; a number of eachtype of defect that originated from one or more collection locations;and a number of each type of defect associated with a particularphlebotomist.

Further, in some aspects, the user interface 218 generates and displaysan image comparison screen for comparing the one or more images of asample to a plurality of reference images. This can be an effective wayof providing a real-time comparison of a sample with references and/orstandards. For example, an image of a sample may be presented in aformat that allows the technician to zoom in on the sample to obtain anoptimal view of a sample color or colors. Further, in some aspects,color values associated with the color arrays and corresponding bloodproperties or defects may be displayed with the sample images. This mayalso provide another quality control opportunity. For example, if acolor value associated with a sample determined to be defective isvisibly incorrect from the perspective of the technologist, thetechnologist's override of the defect may indicate that reference rangesfor the defect should be adjusted. Additionally, compiling results foroverride may provide an indication that one of the sensors, or otherphysical components associated with the system needs to be inspected.

Many different arrangements of the various components depicted, as wellas components not shown, are possible without departing from the spiritand scope of the present invention. Embodiments of the invention havebeen described with the intent to be illustrative rather thanrestrictive. Alternative embodiments may become apparent to thoseskilled in the art that do not depart from its scope. A skilled artisanmay develop alternative means of implementing the aforementionedimprovements without departing from the scope of the invention.

Further, it may be understood that certain features and subcombinationsare of utility, may be employed without reference to other features andsubcombinations, and are contemplated within the scope of the claims.Not all steps listed in the various figures need be carried out in thespecific order described.

What is claimed:
 1. A pre-analytic specimen integrity monitoring devicecomprising: a plurality of sensors configured to capture sensorinformation; a sample extraction device, wherein the sample extractiondevice is configured to remove a blood sample tube from a sample holder,wherein the blood sample tube contains a blood sample, and wherein thesample extraction device further holds the blood sample tube while theplurality of sensors capture the sensor information associated with theblood sample contained within the blood sample tube, the plurality ofsensors comprising: a plurality of imaging sensors comprising at least ared color detector, a blue color detector, and a green color detector,for detecting a plurality of color elements for the blood sample; asample properties processor configured to: process captured sensorinformation from the plurality of sensors, the captured sensorinformation comprising the plurality of color elements and generate oneor more linear color arrays by mapping the plurality of color elementsas a straight line; analyze each of the one or more linear color arraysfrom a first end and from an opposite second end to determine a firstcolor element that is within a valid predetermined brightness range fromthe first end, and a second color element that is within the validpredetermined brightness range from the opposite second end; remove oneor more background color elements from each of the one or more linearcolor arrays to form one or more clean arrays, wherein the one or morebackground color elements include any color elements that are outside ofthe valid predetermined brightness range; store the one or more cleanarrays, and for each clean array in the one or more clean arrays,determine a distance between color elements to generate a distance arrayfor each respective clean array in the one or more clean arrays; storethe distance array generated for each clean array of the one or moreclean arrays; compare each distance in the distance array to atransition indication threshold value to determine one or more colortransitions, wherein the one or more color transitions is/are presentwhen a distance in the distance array is determined to be above thetransition indication threshold value; store the one or more colortransitions determined for the distance array as a transition indicationarray; separate the transition indication array at one or more boundaryregions to form a plurality of individual color arrays, wherein eachcolor element in each of the plurality of individual color arrayscomprises a color value associated with a liquid blood property of theblood sample; compare the color value of each color element in each ofthe plurality of individual color arrays to one or more predeterminedreference ranges; determine that one or more defects are present in theblood sample based on at least one color value of a color element ineach of the plurality of individual color arrays being outside of theone or more predetermined reference ranges; determine, based on theplurality of images of the blood sample, a color of a cap capping theblood sample and a liquid volume of the blood sample; and communicatethe one or more defects identified for the blood sample to auser-interface display device of an automated blood sample processingsystem, wherein the user-interface display device of the automated bloodsample processing system is configured to allow manual inspection of theblood sample by a technician, and is further configured to receive inputfrom the technician with regard to the one or more defects identifiedfor the blood sample, wherein the input received from the technicianincludes an override command to override the one or more defectsidentified for the blood sample.
 2. The pre-analytic specimen integritymonitoring device of claim 1, wherein the presence of the one or moredefects is further determined when the liquid volume of the blood sampleis outside of a predefined volume range for the blood sample.
 3. Thepre-analytic specimen integrity monitoring device of claim 2, whereinthe one or more defects determined by the pre-analytic specimenintegrity monitoring device include one or more of: a sample volumedefect; a hematocrit defect; a hemolysis defect; a clotting defect; anicterus defect; and a lipemia defect.
 4. The pre-analytic specimenintegrity monitoring device of claim 1, wherein the sample extractiondevice is further configured to rotate the blood sample while theplurality of sensors capture the sensor information, and to return theblood sample to the sample holder.
 5. The pre-analytic specimenintegrity monitoring device of claim 1, wherein the pre-analyticspecimen integrity monitoring device is mountable to a track of theautomated blood sample processing system.
 6. The pre-analytic specimenintegrity monitoring device of claim 1, further comprising a samplelighting mechanism for providing light while the sensor information iscaptured by the plurality of sensors.
 7. The pre-analytic specimenintegrity monitoring device of claim 1, further comprising a sample tubewidth detector.
 8. The pre-analytic specimen integrity monitoring deviceof claim 1, further comprising a contour alignment detector formeasuring a tilt angle of the blood sample with respect to the pluralityof imaging sensors.